With rising fossil fuel costs and enhanced environmental concerns,
the use of renewable energy has been widely expounded as a solution to
the challenges of global energy security and climate change. Ireland
currently imports 90 percent of its energy needs and is very vulnerable
to supply disruptions as well as price changes. To relieve
Ireland's dependence on the import of energy, increase its energy
security, and decrease the emission of carbon dioxide, the Irish
government has established sustainable energy goals that will see a
significant increase in the use of renewable energy by 2020. Biomass,
along with wind and hydropower, has been identified as a renewable
energy source with significant promise for reducing dependence on
imported energy (Dennehy et al. 2010). Woody biomass, in particular, has
the potential to play a major role in Ireland's national bioenergy
strategy.

About 10 percent of Ireland is covered by forests. Sitka spruce
(Picea sitchensis (Bong) Carr.), by area and by harvest volume, is
currently Ireland's most important timber species, accounting for
slightly less than 60 percent of the forested area but more than 80
percent of the harvest volume. Phillips (2011) has estimated that the
potential availability of wood fiber for energy in Ireland is currently
over 1 million cubic meters per y. Most of this, in the near future,
could be expected to come from Sitka spruce stands. Small-diameter logs,
as well as forest residue material, are considered to be a potential
source of wood fiber for energy.

High collection and transportation costs, relative to market
values, can be economic barriers to the widespread use of woody biomass
for energy production, however (Rummer 2008). Moisture management,
through storage and drying in the supply chain between harvesting and
use, is key to improving both transportation costs and market values
(Jirjis 1995). Wood is approximately 50 percent water by weight (Klass
1998). Reducing the amount of water, through air drying, reduces
transportation costs (more wood and less water can be delivered per
load) and increases combustion efficiency (less energy is required
during combustion to evaporate water).

A number of studies have compared storage and drying of various
types of woody biomass at various locations (Jirjis 1995, Kent 2008).
Results indicate that storage of uncomminuted logging residues showed
many advantages compared with chip storage; risks of self-ignition were
eliminated, and dry matter losses were minimized (Jirjis 1995). Storage
of uncomminuted residues within a forest environment was less conducive
to rapid drying than storage in exposed sites outside of a forest
environment (Kent 2008).

The optimal storage method, location, and drying time are economic
decisions, however. The benefits obtained from such factors as increased
combustion efficiency and price and reduced transportation costs must be
weighed against increased handling costs (e.g., harvesting machines
having to return to the forest after drying to chip logs, or
intermediate transport to an off-forest storage yard) and opportunity
costs (i.e., capital is tied up in drying material).

Being able to predict the number of days required to reach a
specified moisture content is one of the essential prerequisites for
selecting the optimal drying method and time. Yet there are many factors
that can affect drying rate. A sevenfold change in the rate of moisture
content loss at the beginning (3.5% per week) versus at the end (0.5%
per week) of a 14-week period has been shown for air drying of sugar
maple logs (Acer saccharum Marsh.) in Canada (Rojas et al. 2007) and
indicates a nonlinear trend. Drying rate has also been shown to depend
on species (Stokes et al. 1987, Brand et al. 2011, Nord-Larsen et al.
2011), bark loss (Defo and Brunette 2006), presence of a protective
cover (Jirjis 1995, Nord-Larsen et al. 2011), and season in which drying
began (Stokes et al. 1987, Brand et al. 2011, NordLarsen et al. 2011).
Stokes et al. (1987) produced more than 40 drying rate models for groups
of softwood and hardwood species and for individual species in the
southeastern United States. Many of their models were nonlinear and
depended on number of days since drying began, on rainfall, and on
temperatures. Simpson and Wang (2003) produced two models for air drying
of small ponderosa pine (Pinus ponderosa Douglas ex C. Lawson) and
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs in the western
United States. Their logs were debarked and covered with sheets of
plywood to protect them from rainfall. Their models were based on log
diameter, starting moisture content, relative humidity, and average
temperature. Liang et al. (1996) developed a drying rate model for
bundled Leucaena trees (Leucaena leucocephala (Lain.) de Wit) in Hawaii.
Precipitation and reference evapotranspiration ([ET.sub.0]) were the two
predictive variables in their model. [ET.sub.0] was chosen because it
was used internationally as a measure of moisture loss from crop lands
and it rolls a number of climate variables into one parameter. Because
their model was climate based, they were able to extend their model
spatially to other parts of Hawaii and temporally to different seasons
of the year.

Timing of drying initiation may affect the starting moisture
content for wood as well as the drying rate. However, the literature is
not clear on this. For example, studies in trembling aspen (Populus
tremuloides Michx.) have shown a 9 percent difference in starting
moisture content between summer and winter seasons (Jensen and Davis
1953)--summer being lower--while studies in eastern spruce (Picea spp.)
and balsam fir (Abies balsamea (L.) Mill.) have shown no discernible
difference between seasons (Shottafer and Brackley 1982). Clark and
Gibbs (1957) comment that seasonal differences in moisture content of
freshly felled trees in Canada are likely to be more evident for
hardwoods than softwoods. A recent study of five species in Ireland,
including Sitka spruce, showed a trend for moisture content of freshly
felled trees to peak in the summer months of May to August, fall in
September and October, remain low over winter, and rise again in the
spring (Kent et al. 2009).

Long-term drying studies, extending over 1 to 2 years, have been
carried out in Sitka spruce small log material in Ireland (Kent 2008)
and Scotland (Webster 2006). The author of the Scottish trial noted that
the sites for his trial were in very high rainfall areas and may not be
applicable to other areas of Scotland. For both sets of trials, storage
and drying began in a single season (summer). Data were not collected
for either set of trials that would allow construction of a
climate-based drying model that could be used to extend the results of
the research spatially and temporally.

The review of the literature revealed, among other things, that
air-drying rates for woody biomass depended on the species, season in
which drying began, and the storage system. It showed that air-drying
models, based on climatic factors, could be constructed, and that these
models would allow prediction of drying rates at locations other than
where the data for the models were collected. Finally, it revealed that
an air-drying model for Sitka spruce woody biomass was not available for
use in Ireland.

The objectives of this study were fourfold: (1) to assess optimum
storage systems, which might be used to promote maximum seasoning at
lowest costs, for a range of Sitka spruce woody biomass materials; (2)
to investigate moisture content and climate relationships with the view
to developing a moisture content reduction model based on simple
climatic indicators; (3) to compare the Sitka spruce model with another
readily available model; and (4) to demonstrate how the Sitka spruce
climate-based model might be used to predict moisture content changes
for different locations and timing of harvest in Ireland.

Methods

Objectives I and 2

A storage trial was constructed at Derrygreenagh, near
Rochfortbridge (~100 km to the west of Dublin; 53.4[degrees]N,
7.3[degrees]W) on an open, exposed site to assess the drying potential
of logs moved from the forest environment to a depot (Kofman and Kent
2009b). Eight large steel cradles were constructed and placed on load
cells connected to a data-logger, which recorded weights at hourly
intervals. The cradles were free-draining and were scaled to each hold
at least one full truckload of roundwood, or approximately 30 [m.sup.3]
solid volume. The load cells and data management system were supplied by
Eilersen Electric Digital Systems A/S, Denmark. Approximately 25 tonnes
of logs were placed in each cradle to assess the loss of weight over
time. The assumption was made that any loss of weight would represent a
loss of moisture.

Two types of Sitka spruce material were used in the trial: cleanly delimbed roundwood with an approximate top diameter of 70 mm and
smaller, and crudely delimbed energy wood with no minimum top diameter.
The length of the roundwood material was 3 m, and the length of the
energy-wood material was random lengths up to 4.5 m. All wood was put
into the storage trial within weeks of being harvested. Some of the
cradles were covered with agricultural plastic to keep out the rain.
Most cradles had their cover replaced once during the storage period to
ensure continuity of cover because the original cover degraded. Wood
stored in the cradles was raised 50 cm above ground level, with no
contact between wood and soil. Three types of cover were used: none (on
roundwood only), top only, and top and sides. The combination of
material type and cover provided five treatments. Only one of the
treatments, roundwood with cover, was replicated.

Cradles were filled at different times of the year during 2007 with
freshly felled material to assess the variation in drying seasonally.
Cradles 1 to 7 were filled in the week of April 27, 2007. Cradle 8 was
filled on June 15, 2007. Cradle 1 was emptied on August 31, 2007, and
then refilled with fresh material on September 5, 2007. Cradle 2 was
emptied on December 12, 2007, and then refilled with fresh material on
December 14, 2007. The refilled Cradles 1 and 2 are hereafter referred
to as Cradles 9 and 10, respectively. Cradles 3 to 10 were emptied in
the week of August 11, 2008.

Moisture content (green weight basis) and other material parameters
were sampled at the beginning of the trial. Twenty random sample logs
were measured to establish the degree of delimbing, bark loss, diameter
(top, middle, and butt), length, and weight. Each log was then chipped,
the chips were mixed carefully, and three replicate moisture content
samples were taken, weighed, oven dried, and then reweighed to establish
the starting moisture content. The ending moisture content was
established in a similar manner from 20 random samples of woodchips
gathered periodically because the logs contained in each cradle were
completely chipped at the trial end. Each 50-liter sample was subdivided
into three moisture content samples that were assessed using the ovendry
method.

Moisture content change over the storage trial was calculated from
the recorded weight data and the end dry matter weight derived from the
end weight and end moisture content, as follows:

[M.sub.de] = [W.sub.e] X (100 -[M.sub.e])/100 (1)

[M.sub.n] = [([W.sub.n] - [M.sub.de])/[W.sub.n] X 100 (2)

where

[M.sub.de] = dry matter at trial end (kg), [M.sub.e] = moisture
content at trial end (%, total weight), [M.sub.n] = moisture content at
n time (%, total weight), [W.sub.e] = total log weight, at trial end
(kg), and [W.sub.n] = total log weight, at n time (kg).

Although calculated moisture content data were available for hourly
intervals from when the cradles were loaded, only a subset of these data
was used in this study because first, useful climate data were only
available on a daily basis and, second, biomass supply managers would be
unlikely to need moisture content predictions at hourly, or even daily,
time frames. Moisture content at the end of each 10-day period was,
therefore, used. We created a minimum of 13 and a maximum of 48 records
per cradle. We used a total of 370 records.

Daily climate information was obtained primarily from a portable
Watchdog 2700 weather station (Spectrum Technologies Inc., USA) placed
at the storage site. In addition, an Irish Meteorological Service
climatological station at Derrygreenagh, less than 500 m from the trial
site, provided some data. Data included rainfall (millimeters), mean
wind speed (meters per second), relative humidity (percent), minimum and
maximum temperature (degrees Celsius), and sunshine hours. Daily wind
speed, humidity, temperatures, and sunshine hours were used to calculate
[ET.sub.0] (millimeters) in Microsoft Excel, based on the Food and
Agriculture Organization of the United Nations (FAO) Penman-Monteith
method (FAO 1998). Daily rainfall and [ET.sub.0] data were summed to
provide 10-day rainfall and 10-day [ET.sub.0] data for the same periods
that moisture content records were created.

Moisture content, rainfall, [ET.sub.0], cradle number, and
treatment data were entered in the SAS 9.2 statistical package (SAS
Institute Inc., Cary, North Carolina). Because the data consisted of
repeated measures of moisture content on the same cradles, it was deemed
necessary to fit a mixed effect model to the data. This was done using
the Proc Mixed procedure within SAS with a range of covariance structures. Model selection was based on the overall Akaike information
criterion and on the significance of individual variables. Moisture
content at the end of a 10-day period was predicted as a function of
moisture content at the beginning of the period, rainfall, [ET.sub.0],
and treatment. Days to dry from a starting moisture content to a
specified ending moisture content could then be predicted by stepping
from one 10-day period to the next and finally interpolating between the
last and second-last predictions once the specified moisture content had
been exceeded.

Objective 3

Woody biomass with a maximum moisture content of 30 percent is
preferred for use in small commercial boilers. Drying times to reach 30
percent predicted by the Sitka spruce model were compared with times
from another climate-based drying model used for predictions of small
conifer wood (Simpson and Wang 2003). The latter model, which was
developed in the western United States for predicting drying times for
debarked Douglas-fir logs, used relative humidity and temperature as its
climate variables. Rainfall was not included in the model. The logs
stacks were stored outside but had a sheet of plywood on top of the
stack. Logs within the stacks were separated by wooden stickers.

Objective 4

The Sitka spruce model was also used to predict drying times, based
on historical climate data, for storage initiated in four seasons
(summer, autumn, winter, and spring) at four additional locations around
Ireland: Knock, Ballyhaise, Oakpark, and Valentia (Fig. 1). These sites
were selected because they represent a range of climatic conditions in
Ireland. Historical data were obtained from the Irish Meteorological
Service. Table 1 shows the rainfall and [ET.sub.0] for these sites for a
similar period to that of the drying storage trial at Derrygreenagh.

Two sets of drying times were predicted for these sites: one set
where the assumed starting moisture content was the same for all seasons
(57.5%) and one set where the starting moisture content varied with
season (summer, 61%; autumn and spring, 58%; and winter, 55%). The first
set was representative of the average starting moisture contents from
all cradles for the storage trial. The second set was representative of
results reported by Kent et al. (2009), who noted a trend in seasonal
moisture content of felled Sitka spruce.

[FIGURE 1 OMITTED]

Results and Discussion Objectives I and 2

Table 2 provides an overview of the treatments and the woody
biomass characteristics at the beginning and end of the trial. At the
start of the trial, each cradle contained a minimum of 345 and a maximum
of 832 logs. Initial green weights per cradle ranged from 20 to 28
tonnes. Starting moisture contents ranged from 51.6 to 61.2 percent and
averaged 57.5 percent. Ending moisture contents ranged from 18.4 to 24.4
percent and averaged 21.1 percent. These ending moisture contents were
not the lowest (16.8%) achieved during the trial, however, because
cradles would absorb moisture during periods of high rainfall as well as
lose moisture during periods of high [ET.sub.0]. This can be seen in
Figure 2, which shows drying curves for one of the treatments (roundwood
with top cover) initiated at four different times of the year.

The number of weeks required to dry Sitka spruce woody biomass
below 30 percent ranged from 14 to 26 weeks for an off-forest storage
yard. The season that storage began had some effect on this duration;
shorter durations tended to be associated with spring and summer
seasons, and longer durations tended to be associated with autumn and
winter seasons. However, the longest drying time was associated with a
cradle where drying was initiated in summer.

It should be noted that these drying rates are much faster than
were found by Kofman and Kent (2009a) for Sitka spruce woody biomass
stored in forest environments in Ireland; similar procedures for
assessing starting and ending moisture contents were used in both
studies. Even after more than 450 days of drying in the forest, no
material had dried below 30 percent moisture content, and few wood
stacks had dried below 45 percent. Material in this off-forest storage
trial dried to 45 percent moisture content in 9 to 10 weeks, whereas it
took 80 weeks for similar material to reach this moisture content in
Kent and Kofman's in-forest storage trial.

A mixed model, with a heterogeneous compound symmetry covariance
structure, was found to fit the data best:

Treatments 1 through 5 were roundwood with top cover, energy wood
with top and side cover, roundwood with top and side cover, energy wood
with top cover, and roundwood with no cover, respectively. Treatment 1
was the base treatment and had the most data points associated with it.

The model indicates, as one would expect, that rainfall increases
the biomass moisture content and [ET.sub.0] decreases it. The model also
indicates that the moisture content at the beginning of a 10-day drying
period positively influences the moisture content at the end of the
10-day period and that the influence is nonlinear. Finally, the model
indicates that all treatments were significantly different from each
other and that there were interactions between treatments and moisture
content at the beginning of a 10-day drying period. Because of the
nonlinear interactions between treatments and starting moisture content,
it is not obvious, without reference to the model, how treatments
compare with each other for the same conditions.

Figure 3 demonstrates how well the model fit the drying curves for
2 of the 10 cradles. The two chosen are those with the best and worst
fit, based on predicted versus actual number of days to achieve 30
percent moisture content. Predicted drying times for 9 of the 10 cradles
were within 10 percent of the actual number of days and no worse than 15
days. Predicted drying time for one of the cradles was underestimated by
25 percent (44 d); however, the predicted time for this cradle would
have been within 10 percent of the actual number of days but for an
extended period of rain beginning on the day the actual moisture content
was at 30.1 percent.

The effect of treatments, that is, biomass type and type of cover,
was determined by setting the initial moisture content for all
treatments to 57.5 percent (the average for all cradles) and running the
model using the same climatic conditions (drying beginning at the
beginning of June 2007). Figure 4 shows the drying curves for the five
treatments. There was about a 160-day difference between the minimum
time and the maximum time required to dry biomass to 30 percent moisture
content.

[FIGURE 2 OMITTED]

Unexpectedly, energy wood took longer (-110 d) than roundwood for
the same types of cover. Lumber drying research would indicate the
opposite, that is, small-diameter woody material would be expected to
dry at a faster rate than thicker material (Simpson et al. 1999). The
reverse trend may have been due to the presence of bark; bark cover was
40 percent greater on average on the energy wood than on the roundwood.
Defo and Bnmette (2006) have shown that drying rate is negatively
affected by the amount of bark present.

Energy wood with top and side cover dried faster (-70 d) than
energy wood with a top cover but no side cover. Roundwood with a top
cover dried faster (-20 d) than roundwood with no cover. Unexpectedly,
roundwood with top and side cover dried at almost the same rate as
roundwood with no cover. We have no explanation for this, although the
model output reflected the observed similar drying rates of the two
trial cradles.

Objective 3

An evaluation of an alternative and readily available climate-based
drying model showed that it was inappropriate for predicting drying
times for Sitka spruce in Ireland. Simpson and Wang's Douglas-fir
drying model (2003) predicted times that ranged from 7 to 12 days for
the cradles containing the roundwood with top cover treatments to dry to
30 percent moisture content for the same humidity and temperature
conditions recorded at the Derrygreenagh site in Ireland. These
predictions underestimate drying times of Sitka spruce biomass material
in Ireland by a factor of about 10. These results highlight (1) the
importance of including rainfall in Irish drying models and (2) how
debarking and filleting of logs can speed up the drying process.

[FIGURE 3 OMITTED]

Objective 4

The results shown in Table 3 demonstrate how an appropriate Sitka
spruce climate-based model could be used to predict drying times for
different seasons in which drying was initiated and for different
locations. If there is no difference between starting moisture
conditions, as indicated in some reports for softwoods (Clark and Gibbs
1957, Shottafer and Brackley 1982), the year can be split into two
periods from the perspective of drying rates: spring/summer harvesting
requiring 30 to 50 fewer drying days than autumn/winter for the five
locations. If there is a difference in starting moisture conditions for
Sitka spruce, as reported by Kent et al. (2009), initiating drying in
spring would lead to the fastest drying rates for all five locations.
Summer, winter, and then autumn is the order of the drying rates for the
remaining seasons in which drying is initiated.

[FIGURE 4 OMITTED]

There was about 16 days' difference in predicted drying rates
between the fastest (Oakpark) and slowest (Derrygreenagh) locations for
the summer, winter, and spring harvesting seasons, and 27 days'
difference for the autumn harvesting season. Looked at in a different
way, the difference between the shortest (spring harvesting) and longest
(autumn) drying time for a given site was 45 to 50 days for the Knock,
Valentia, and Derrygreenagh sites and 30 to 35 days for the Oakpark and
Ballyhaise sites. The Knock and Valentia sites had the highest
rainfalls. The Derrygreenagh site was characterized by high rainfall and
the lowest [ET.sub.0]. The Oakpark and Ballyhaise sites had
comparatively low rainfall and high [ET.sub.0].

Concluding Remarks

The application of mixed effects regression modeling to air-drying
data for Sitka spruce logs has resulted in a method for estimating
drying times of covered and uncovered roundwood and energy-wood logs
stacked any day of the year at any off-forest location in Ireland where
historic weather data are available. The regression model constructed
relates moisture content loss over a 10-day interval to moisture content
at the start of the interval, cumulative precipitation and [ET.sub.0]
for the period, woody biomass type, and type of cover. The model was
easily used in a spreadsheet to estimate drying times for different
locations in Ireland, different starting seasons for drying, different
biomass types, and different types of cover. The predicted number of
days was determined via a chained procedure in increments of l0 days.
The calculated final moisture content at the end of each 10-day interval
became the initial moisture content at the beginning of the next 10day
interval. This calculation was repeated for the period of interest or
until the target moisture content had been reached. The model provided
more accurate estimates of drying rates than an alternative model
developed for off forest drying of small conifer logs in the western
United States. The model should not be used for estimating drying times
for in-forest biomass storage because it will overestimate drying rates
considerably.

Acknowledgments

Funding for this project has been provided by grants from COFORD,
within the Department of Agriculture, Fisheries and Food (Ireland), from
the USDA Special Grant for Wood Utilization Research, from the Oregon
State University Stewart Professorship in Forest Engineering, and from
Waiariki Institute of Technology (New Zealand). The authors also
acknowledge the support of Bordna Mona, the Irish state peat company,
for constructing the storage cradles and hosting the storage trial.